{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nptdms import TdmsFile\n",
    "import matplotlib.pyplot as plt\n",
    "import ruptures as rpt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import scipy.cluster.hierarchy as hac\n",
    "import math\n",
    "import numpy as np\n",
    "from random import sample\n",
    "from scipy.stats import mode\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "from scipy.cluster.hierarchy import fcluster\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SegHub(object):\n",
    "\n",
    "    def segment_and_plot(self, df, model='l1', count=0, n_bkps=5):\n",
    "        '''\n",
    "        recieves a segment as input and runs ruptures changeppoint detection algorithm to segment a\n",
    "        punch into subsequences. Eventually, it plots the segments,\n",
    "        saves the plot and returns a dataframe containing all segments numbered begining with 1\n",
    "        '''\n",
    "        dfs = pd.DataFrame()\n",
    "        lower_bound = 0\n",
    "        upper_bound = len(df)\n",
    "\n",
    "        # change point detection\n",
    "        #model = \"l1\"  # \"l2\", \"rbf\"\n",
    "\n",
    "        data = df[lower_bound:upper_bound].values\n",
    "        algo = rpt.Dynp(model=model, min_size=15, jump=15).fit(data)\n",
    "        my_bkps_t = algo.predict(n_bkps)\n",
    "\n",
    "        j = 0\n",
    "        lower_bound_temp = lower_bound\n",
    "     \n",
    "        while j < n_bkps :\n",
    "            df_temp = pd.DataFrame({j: df[my_bkps_t[j]:my_bkps_t[j+1]]})\n",
    "            df_temp = df_temp.reset_index(drop=True)\n",
    "            dfs = pd.concat([dfs, df_temp], axis=1)\n",
    "            #df[lower_bound_temp:my_bkps_t[j]+lower_bound].plot()\n",
    "            #lower_bound_temp = my_bkps_t[j]+lower_bound\n",
    "            j = j + 1\n",
    "        #plt.savefig('plot_' + model + '_' + str(count) + '.png')\n",
    "        #plt.clf()\n",
    "        #print(dfs)\n",
    "        return dfs\n",
    "\n",
    "    def extract_hub(self, df_force, df_stroke, start=3000, threshold=0.51):\n",
    "        '''\n",
    "        returns data frame containing each punch segment derived by the df_stroke timeseries data\n",
    "        '''\n",
    "        dfs = pd.DataFrame()\n",
    "        flag = False\n",
    "        begin_temp = 0\n",
    "        count = 0\n",
    "        end = len(df_stroke)\n",
    "        for i in range(start, end):\n",
    "            if not(flag) and df_stroke[i] > threshold:\n",
    "                flag = True\n",
    "                begin_temp = i\n",
    "            elif flag and df_stroke[i] > threshold:\n",
    "                continue\n",
    "            elif flag and df_stroke[i] < threshold:\n",
    "                #print(begin_temp,i)\n",
    "                seg_temp = df_force[begin_temp:i].reset_index(drop=True)\n",
    "                #seg_temp = (begin_temp, i)\n",
    "                #segments.append(seg_temp)\n",
    "                df_temp= pd.DataFrame({count: seg_temp})\n",
    "                #df_temp = pd.DataFrame(seg_temp)\n",
    "                #df_temp = df_temp.reset_index(drop=True)\n",
    "                dfs = pd.concat([dfs,df_temp], axis =1)\n",
    "                flag = False\n",
    "                count = count + 1\n",
    "            else:\n",
    "                continue\n",
    "        return dfs\n",
    "\n",
    "\n",
    "    def plot_all(df_to_plot):\n",
    "        '''\n",
    "        plots all columns in the dataframe\n",
    "        '''\n",
    "        ax = None\n",
    "        for index in df_to_plot:\n",
    "            ax = sns.tsplot(ax=ax, data=df_to_plot[index].values, err_style=\"unit_traces\")\n",
    "        plt.savefig('test.png')\n",
    "\n",
    "    def separate_subsequence(df_segments, target_segment):\n",
    "        dfs = pd.DataFrame()\n",
    "        \n",
    "        for j in range(0, len(df_segments)):\n",
    "            df_temp = pd.DataFrame({j: df_segments[j]})\n",
    "            df_temp = df_temp.reset_index(drop=True)   \n",
    "            dfs = pd.concat([dfs, df_temp], axis=1)\n",
    "\n",
    "    def DTWDistance(self, s1, s2):\n",
    "        '''\n",
    "        plots all columns in the dataframe\n",
    "        '''\n",
    "        DTW = {}\n",
    "\n",
    "        for i in range(len(s1)):\n",
    "            DTW[(i, -1)] = float('inf')\n",
    "        for i in range(len(s2)):\n",
    "            DTW[(-1, i)] = float('inf')\n",
    "        DTW[(-1, -1)] = 0\n",
    "\n",
    "        for i in range(len(s1)):\n",
    "            for j in range(len(s2)):\n",
    "                dist = (s1[i]-s2[j])**2\n",
    "                DTW[(i, j)] = dist + min(DTW[(i-1, j)],DTW[(i, j-1)], DTW[(i-1, j-1)])\n",
    "\n",
    "        return math.sqrt(DTW[len(s1)-1, len(s2)-1])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Read in data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in data, take the TDMS data type as example\n",
    "tdms_file = TdmsFile(\".\\\\AKF_SS-FW2-H04521-H05000.tdms\")\n",
    "# tdms_file.groups()\n",
    "df_all = tdms_file.object('Untitled').as_dataframe()\n",
    "df_force = df_all['Stempel_1 (Formula Result)']\n",
    "df_stroke = df_all['Position_Ma']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Extract each punch shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEH = SegHub()\n",
    "# Init lists and dfs\n",
    "segmentation_1 = []\n",
    "segmentation_2 = []\n",
    "segmentation_3 = []\n",
    "segmentation_4 = []\n",
    "segmentation_5 = []\n",
    "# Extract all punches of the dataset\n",
    "df_punches = SEH.extract_hub(df_force, df_stroke)\n",
    "#print(df_punches.describe())\n",
    "df_punches = df_punches.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: separate into subsegmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x=0\n",
    "for i in df_punches:\n",
    "\n",
    "    # first changepoint detection on whole punch\n",
    "    # second changepoint detection on \n",
    "    punch_seg = SEH.segment_and_plot(df_punches[i].dropna(), 'l2')\n",
    "    #sub_punch_seg = SEH.segment_and_plot(punch_seg[6].dropna(), 'rbf', 7 + i, 4)\n",
    "\n",
    "    # append to corresponding list\n",
    "    segmentation_1.append(np.asarray(punch_seg[0]))\n",
    "    segmentation_2.append(np.asarray(punch_seg[1]))\n",
    "    segmentation_3.append(np.asarray(punch_seg[2]))\n",
    "    segmentation_4.append(np.asarray(punch_seg[3]))\n",
    "    segmentation_5.append(np.asarray(punch_seg[4]))\n",
    "    #sub_segmentation.append(sub_punch_seg)\n",
    "    x = 1+x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_punch_1 =[]\n",
    "sub_punch_2 =[]\n",
    "sub_punch_3 =[]\n",
    "sub_punch_4 =[]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "x =0\n",
    "for i in segmentation_5:\n",
    "    sub_punch_seg = SEH.segment_and_plot(pd.DataFrame(i)[0].dropna(), 'rbf', 7 + i, 4)\n",
    "    # append to corresponding list\n",
    "    sub_punch_1.append(np.asarray(sub_punch_seg[0]))\n",
    "    sub_punch_2.append(np.asarray(sub_punch_seg[1]))\n",
    "    sub_punch_3.append(np.asarray(sub_punch_seg[2]))\n",
    "    sub_punch_4.append(np.asarray(sub_punch_seg[3]))\n",
    "    x = x+1\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(segmentation_1).to_csv(\"segmentation_0.csv\",index=False)\n",
    "pd.DataFrame(segmentation_2).to_csv(\"segmentation_1.csv\",index=False)\n",
    "pd.DataFrame(segmentation_3).to_csv(\"segmentation_2.csv\",index=False)\n",
    "pd.DataFrame(segmentation_4).to_csv(\"segmentation_3.csv\",index=False)\n",
    "pd.DataFrame(segmentation_5).to_csv(\"segmentation_4.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  3.1. save the segmented data and read from date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sss.tocsv(\"xxx.csv\")\n",
    "segmentations=[[],[],[],[],[],[],[],[],[]]\n",
    "segmentations[0] = pd.read_csv(\"segmentation_0.csv\")\n",
    "segmentations[1] = pd.read_csv(\"segmentation_1.csv\")\n",
    "segmentations[2] = pd.read_csv(\"segmentation_2.csv\")\n",
    "segmentations[3] = pd.read_csv(\"segmentation_3.csv\")\n",
    "#segmentations[4] = pd.read_csv(\"segmentation_4.csv\")\n",
    "# the following is the sub-sequence\n",
    "segmentations[4] = pd.read_csv(\"sub_punch_0.csv\")\n",
    "segmentations[5] = pd.read_csv(\"sub_punch_1.csv\")\n",
    "segmentations[6] = pd.read_csv(\"sub_punch_2.csv\")\n",
    "segmentations[7] = pd.read_csv(\"sub_punch_3.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4. Clustering \n",
    "## 4.1 data preparation: uniform the length of segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d5ffacfdd8>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d5ff254a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before uniforming:\n",
    "# for example: segmentation _1\n",
    "plt.plot(segmentations[2].count(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Uniformation(Seg):\n",
    "    if len(Seg.shape) == 1:\n",
    "        return Seg.dropna()\n",
    "        \n",
    "    lens = Seg.count(axis=1) # get the number of non-nan length\n",
    "    len_mnum = mode(lens)[0][0]\n",
    "    len_min = min(lens)\n",
    "    rest = Seg.iloc[:,0:len_mnum]\n",
    "    if len_mnum == len_min:\n",
    "        return rest\n",
    "    else:\n",
    "        '''\n",
    "        for the length longerthan the len_mnum, we directly shorten them into mode\n",
    "        for the length smaller than the len_mnum, we use the interpolate to compansate.\n",
    "        '''\n",
    "        rows = np.flatnonzero(lens<len_mnum)\n",
    "        rest.iloc[rows,0:len_mnum] = rest.iloc[rows,0:len_mnum].interpolate(method='linear',\n",
    "                                                                            downcast = 'infer',axis = 1 )\n",
    "        return rest\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 sample the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly sample 80% of your dataframe\n",
    "df_percent = data_seg_1.sample(frac=0.8)\n",
    "# the df_rest used to test\n",
    "df_rest = data_seg_1.iloc[~data_seg_1.index.isin(df_percent.index)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "z1 = hac.linkage(data_seg_1, 'ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d5fe770208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('distance')\n",
    "hac.dendrogram(\n",
    "    z1,\n",
    "    leaf_rotation=90.,  # rotates the x axis labels\n",
    "    leaf_font_size=12.,  # font size for the x axis labels\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d5ff3ef518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('sample index')\n",
    "plt.ylabel('distance')\n",
    "hac.dendrogram(\n",
    "    z1,\n",
    "    truncate_mode = 'lastp',\n",
    "    p = 10,\n",
    "    leaf_rotation=90.,  # rotates the x axis labels\n",
    "    leaf_font_size=12.,  # font size for the x axis labels\n",
    "    show_contracted=True,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_clusters(timeSeries, z, k, plot=False):\n",
    "    # k Number of clusters I'd like to extract\n",
    "    results = fcluster(z, k, criterion='maxclust')\n",
    "\n",
    "    # check the results\n",
    "    s = pd.Series(results)\n",
    "    clusters = s.unique()\n",
    "\n",
    "    for c in clusters:\n",
    "        cluster_indeces = s[s==c].index\n",
    "        print(\"Cluster %d number of entries %d\" % (c, len(cluster_indeces)))\n",
    "        if len(cluster_indeces)==0:\n",
    "            continue\n",
    "        else:\n",
    "            if plot:\n",
    "                timeSeries.T.iloc[:,cluster_indeces].plot()\n",
    "                plt.show()\n",
    "    return results\n",
    "\n",
    "\n",
    "#clusters1 = print_clusters(data_seg_1, z1, 3, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def classifier(df_train, clusters, df_test, n=3):\n",
    "    neigh = KNN(n_neighbors=n)\n",
    "    neigh.fit(df_train, clusters) \n",
    "    df_test\n",
    "    classes1 = neigh.predict(df_test)\n",
    "\n",
    "    s = pd.Series(classes1)\n",
    "\n",
    "    for c in range(1,n+1):\n",
    "        cluster_indeces = s[s==c].index\n",
    "        lens = len(cluster_indeces)\n",
    "        if lens:\n",
    "            print(\"Classified into Cluster %d \" % (c))\n",
    "            df_test.T.iloc[:,cluster_indeces].plot()\n",
    "            plt.show()\n",
    "######################\n",
    "    \n",
    "#classifier(data_seg_1, clusters1, df_rest,3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#classifier(data_seg_1, clusters, df_rest,3)\n",
    "test = pd.DataFrame([s,s]).loc[:,0:150]\n",
    "classifier(data_train,clusters,test ,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 run through all the data set and save into files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\xiaoli yang\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "c:\\users\\xiaoli yang\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Uniformation + cluster + save in the file\n",
    "data_seg = [[],[],[],[],[],[],[],[],[],[]]\n",
    "for i in range (0,8):\n",
    "    data_seg[i]= Uniformation(segmentations[i])\n",
    "    z = hac.linkage(data_seg[i], 'ward')\n",
    "    pd.DataFrame(z).to_csv(\"cluster_\"+str(i)+\".csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 the Overall Testing\n",
    "\n",
    "we simulate when a sequence of 'punch' data coming in what our framework would react"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18b977bde80>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b977bdda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample time series data \n",
    "df_f = df_force[80800:99000].reset_index(drop=True)\n",
    "df_s = df_stroke[80800:99000].reset_index(drop=True)\n",
    "df_f.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step 2: extract the hub \n",
    "SEH = SegHub()\n",
    "# Init lists and dfs\n",
    "\n",
    "# Extract all punches of the dataset\n",
    "df_punches_t = SEH.extract_hub(df_f, df_s)\n",
    "#print(df_punches.describe())\n",
    "df_punches_t = df_punches_t.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: segmentation into trends\n",
    "punch_seg = SEH.segment_and_plot(df_punches_t[0].dropna(), 'l2')\n",
    "sub_punch_seg = SEH.segment_and_plot(punch_seg[4].dropna(), 'rbf', 7 + i, 4)\n",
    "punch_seg[4] = sub_punch_seg[0]\n",
    "punch_seg[5] = sub_punch_seg[1]\n",
    "punch_seg[6] = sub_punch_seg[2]\n",
    "punch_seg[7] = sub_punch_seg[3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:1\n",
      "Cluster 2 number of entries 242\n",
      "Cluster 3 number of entries 712\n",
      "Cluster 1 number of entries 2\n",
      "Result:.........\n",
      "Classified into Cluster 2 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18ba163beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:2\n",
      "Cluster 2 number of entries 392\n",
      "Cluster 3 number of entries 562\n",
      "Cluster 1 number of entries 2\n",
      "Result:.........\n",
      "Classified into Cluster 2 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18ba163bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:3\n",
      "Cluster 3 number of entries 792\n",
      "Cluster 2 number of entries 162\n",
      "Cluster 1 number of entries 2\n",
      "Result:.........\n",
      "Classified into Cluster 3 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18ba1636d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:4\n",
      "Cluster 2 number of entries 338\n",
      "Cluster 3 number of entries 616\n",
      "Cluster 1 number of entries 2\n",
      "Result:.........\n",
      "Classified into Cluster 2 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b9730b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:5\n",
      "Cluster 3 number of entries 195\n",
      "Cluster 2 number of entries 290\n",
      "Cluster 1 number of entries 472\n",
      "Result:.........\n",
      "Classified into Cluster 3 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b96b2cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:6\n",
      "Cluster 1 number of entries 589\n",
      "Cluster 3 number of entries 366\n",
      "Cluster 2 number of entries 2\n",
      "Result:.........\n",
      "Classified into Cluster 3 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b956606a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:7\n",
      "Cluster 1 number of entries 561\n",
      "Cluster 3 number of entries 394\n",
      "Cluster 2 number of entries 2\n",
      "Result:.........\n",
      "Classified into Cluster 3 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b971bf6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend:8\n",
      "Cluster 3 number of entries 473\n",
      "Cluster 1 number of entries 328\n",
      "Cluster 2 number of entries 156\n",
      "Result:.........\n",
      "Classified into Cluster 3 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18b971c6ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 4: classification\n",
    "for i in range(0,8):\n",
    "    print(\"Trend:\"+str(i+1))\n",
    "    s = Uniformation(punch_seg[i])\n",
    "    z = pd.read_csv(\"cluster_\"+str(i)+\".csv\")\n",
    "    data_train= Uniformation(segmentations[i])\n",
    "    row,col=data_train.shape\n",
    "    col= min(len(s),col)\n",
    "    clusters = print_clusters(data_train, z, 3, plot=False)\n",
    "    print(\"Result:.........\")\n",
    "    s = s[0:col]\n",
    "    test = pd.DataFrame([s,s])\n",
    "    data_train = data_train.iloc[:,0:col]\n",
    "    # generate new clusters and save into the file\n",
    "    new_dataset = data_train.copy()\n",
    "    new_dataset.loc[row] = s.values\n",
    "    z = hac.linkage(new_dataset, 'ward')\n",
    "    pd.DataFrame(z).to_csv(\"cluster_\"+str(i)+\".csv\",index=False)\n",
    "    #test= s.values\n",
    "    classifier(data_train,clusters,test ,3)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to do:  save the test also to segmentation data, since here we use the fake new data, so we don't save to the file"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
